Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,684
datasets available to search
ShareScore release 0.9.0
Dataset results
3,684 results for “extract”
Extracts from Paquet et al., 2020, Dreaming
Open the record for dataset details and reuse information.
Extracts from Mariani et al., 2023, Psychoanal Psychol
Open the record for dataset details and reuse information.
Extracts from Cariola, 2014, Imagin Cogn Pers
Open the record for dataset details and reuse information.
Extracts from Hawkins & Boyd, 2017, Dreaming
Open the record for dataset details and reuse information.
Extracts from Meador et al., 2022, Appl Cognit Psychol
Open the record for dataset details and reuse information.
Extracts from McNamara et al., 2015, Dreaming
Open the record for dataset details and reuse information.
Extracts from Cariola, 2010, unpublished
Open the record for dataset details and reuse information.
Extracts from Pennebaker & Francis, 1999, LIWC Manual
Open the record for dataset details and reuse information.
Extracts from Pennebaker et al., 2007, LIWC Manual
Open the record for dataset details and reuse information.
Extracts from Pennebaker et al., 2001, LIWC Manual
Open the record for dataset details and reuse information.
Extracts from Pennebaker et al., 2015, LIWC Manual
Open the record for dataset details and reuse information.
Data from: How many specimens make a sufficient training set for automated three dimensional feature extraction?
<p>Deep learning has emerged as a robust tool for automating feature extraction from 3D images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artificial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images with eight specimens being required to achieve 95% accuracy. Furthermore, data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared to the external structure, due to low contrast differences between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from 3D images. </p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Figure 2 in Phytochemical characterization, and antioxidant and antibacterial activities of the hydroethanolic extract of Anadenanthera peregrina stem bark
Figure 2. HPLC-PDA chromatographic profiles (λ = 254 nm) of: (A) sample extract; (B) gallic acid standard; (C) catechin standard; and (D) epicatechin standard, followed by UV spectra (190-400 nm).
Figure 1 in Phytochemical characterization, and antioxidant and antibacterial activities of the hydroethanolic extract of Anadenanthera peregrina stem bark
Figure 1. Erythrocyte hemolysis in a 5% red blood cell suspension by the hydroethanolic extract of A. peregrina stem bark. (A) 5% suspension of red blood cells; (B) hemolysis after 1 min of reaction; (C) advanced hemolysis after 5 min; and (D) completely hemolyzed red blood cells within 10 min of reaction. Bars: At (A) 1.000×; (B) 500×; (C) 650×; and (D) 1.800×.
Fig. 4 in Bactericidal properties of mangrove Bruguiera cylindrica (L.) Blume leaf and Rhizophora mucronata Poir. stilt root extracts on Vibrio cholera, MTCC 435 and Escherichia coli pathogens
Fig. 4 — Percentage viability of HeLa cells with different mangrove extracts. Values are mean of triplicate reading (mean±SD)
Fig. 3 in Bactericidal properties of mangrove Bruguiera cylindrica (L.) Blume leaf and Rhizophora mucronata Poir. stilt root extracts on Vibrio cholera, MTCC 435 and Escherichia coli pathogens
Fig. 3 — SEM images of mangrove extracts with treated and un-treated bacterial cells: (a) Un-treated; (b) MTCC 435 treated with R. mucronata acetone stilt root extract; (c) MTCC 435 treated with B. cylindrica acetone leaf extract; (d) Un-treated Escherichia coli; (e) Escherichia coli treated with R. mucronata acetone stilt root extract; (f) Escherichia coli treated with B. cylindrica ethyl acetate leaf extract; (g) Un-treated Vibrio cholera; and (h) Vibrio cholerae treated with R. mucronata acetone stilt root extract
Fig. 2 — Chromatogram for A in Antimicrobial activity of the crude peptide extracts from Blackfin sea catfish Arius jella Day, 1877
Fig. 2 — Chromatogram for A. jella peptide extract using FPLC: a) 5 % Sep-Pak fraction, b) 40 % Sep-Pak fraction, and c) 80 % Sep-Pak fraction
Fig. 2 in Bactericidal properties of mangrove Bruguiera cylindrica (L.) Blume leaf and Rhizophora mucronata Poir. stilt root extracts on Vibrio cholera, MTCC 435 and Escherichia coli pathogens
Fig. 2 — Viable count analysis of the bacterial pathogens - I: Collected solvent extract; II: Control plates [a) MTCC 435, g) E. coli, m) V. cholerae; b, h & n: Tetracycline (40 µg.ml-1) treated plates - (b) MTCC 435, h) E. coli, and n) V. cholerae); c, e, i, k & o: X- MBC concentration treated plates - (c) MTCC 435 with R. mucronata acetone stilt root extract, e) MTCC 435 with B. cylindrica acetone leaf extract, i) E. coli with R. mucronata acetone stilt root extract, k) E. coli with B. cylindrica acetone leaf extract, and o) V. cholerae with B. cylindrica ethyl acetate leaf extract); d, f, j, l & p: 2X-MBC concentration treated plates - (d) MTCC 435 with R. mucronata acetone stilt root extract, f) MTCC 435 with B. cylindrica acetone leaf extract, j) E. coli with R. mucronata acetone stilt root extract, l) E. coli with B. cylindrica acetone leaf extract, and p) V. cholerae with B. cylindrica ethyl acetate leaf extract)]
Fig. 1 in Bactericidal properties of mangrove Bruguiera cylindrica (L.) Blume leaf and Rhizophora mucronata Poir. stilt root extracts on Vibrio cholera, MTCC 435 and Escherichia coli pathogens
Fig. 1 — Effect of various extracts on growth pattern of clinical pathogens: a) R. mucronata acetone stilt root extract with MTCC 435; b) B. cylindrica acetone leaf extract with MTCC 435; c) R. mucronata acetone stilt root extract with E. coli; d) B. cylindrica acetone leaf extract with E. coli; and e) B. cylindrica ethyl acetate leaf extract with V. cholera
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.